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Obstacle-avoiding path planning for multiple autonomous underwater vehicles with simultaneous arrival

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Abstract

This paper focuses on planning the obstacle-avoiding paths of multiple autonomous underwater vehicles (AUVs) in complex ocean environment, with the time coordination of simultaneous arrival. By imitating the nature phenomenon that river water avoids rocks and reaches the destination, the interfered fluid dynamical system (IFDS) is first presented to obtain the single-AUV path for obstacle avoidance, where the modulation matrix is calculated to quantify the influence of obstacles especially. Then the two-layer comprehensive adjustment to path length and voyage speed is utilized, aiming to achieve the simultaneous arrival at destination between multi-AUVs. By adjusting reactive parameters of IFDS, the former is to roughly ensure the intersection of AUVs’ potential arrival time range to be non-null. On this basis, the latter adjusts each AUV’s voyage speed finely using the consensus method with state predictor, which has faster convergence speed. If the multi-AUVs communication network is connected, the whole system will quickly converge to the consensus state, i.e., the estimated time of arrival (ETA) of each AUV tends to be equal. Finally, the simulation results verify the advantages of our proposed method.

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References

  1. Wynn R B, Huvenne V A I, Le Bas T P, et al. Autonomous underwater vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience. Mar Geol, 2014, 352: 451–468

    Article  Google Scholar 

  2. Fischer N, Hughes D, Walters P, et al. Nonlinear RISE-based control of an autonomous underwater vehicle. IEEE Trans Robot, 2014, 30: 845–852

    Article  Google Scholar 

  3. Zeng Z, Lian L, Sammut K, et al. A survey on path planning for persistent autonomy of autonomous underwater vehicles. Ocean Eng, 2015, 110: 303–313

    Article  Google Scholar 

  4. Garau B, Bonet M, Alvarez A, et al. Path planning for autonomous underwater vehicles in realistic oceanic current fields: Application to gliders in the western Mediterranean Sea. J Mar Res, 2014, 6: 5–22

    Google Scholar 

  5. Ferguson D, Stentz A. Using interpolation to improve path planning: The Field D* algorithm. J Field Robot, 2006, 23: 79–101

    Article  MATH  Google Scholar 

  6. Petres C, Pailhas Y, Patron P, et al. Path planning for autonomous underwater vehicles. IEEE Trans Robot, 2007, 23: 331–341

    Article  Google Scholar 

  7. Soulignac M. Feasible and optimal path planning in strong current fields. IEEE Trans Robot, 2011, 27: 89–98

    Article  Google Scholar 

  8. Pereira A A, Binney J, Hollinger G A, et al. Risk-aware path planning for autonomous underwater vehicles using predictive ocean models. J Field Robot, 2013, 30: 741–762

    Article  Google Scholar 

  9. Fu Y, Ding M, Zhou C. Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE Trans Syst Man Cybern A, 2012, 42: 511–526

    Article  Google Scholar 

  10. Roberge V, Tarbouchi M, Labonte G. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans Ind Inf, 2013, 9: 132–141

    Article  Google Scholar 

  11. Yao P, Wang H. Dynamic adaptive ant lion optimizer applied to route planning for unmanned aerial vehicle. Soft Comput, 2017, 21: 5475–5488

    Article  Google Scholar 

  12. Zeng Z, Lammas A, Sammut K, et al. Shell space decomposition based path planning for AUVs operating in a variable environment. Ocean Eng, 2014, 91: 181–195

    Article  Google Scholar 

  13. Hernandez E, Carreras M, Ridao P. A path planning algorithm for an AUV guided with homotopy classes. In: Proceedings of the Twenty- First International Conference on Automated Planning and Scheduling, 2011. 82–89

    Google Scholar 

  14. McMahon J, Plaku E. Mission and motion planning for autonomous underwater vehicles operating in spatially and temporally complex environments. IEEE J Ocean Eng, 2016, 41: 893–912

    Article  Google Scholar 

  15. Hernández J D, Vidal E, Vallicrosa G, et al. Online path planning for autonomous underwater vehicles in unknown environments. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). Washington, 2015. 1152–1157

    Chapter  Google Scholar 

  16. Caldwell C V, Dunlap D D, Collins E G. Motion planning for an autonomous underwater vehicle via sampling based model predictive control. In: Proceedings of MTS/IEEE OCEANS Conference. Washington, 2010. 1–6

    Google Scholar 

  17. Yilmaz N K, Evangelinos C, Lermusiaux P, et al. Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming. IEEE J Ocean Eng, 2008, 33: 522–537

    Article  Google Scholar 

  18. Liu M, Xu B, Peng X. Cooperative path planning for multi-AUV in time-varying ocean flows. J Syst Eng Electron, 2016, 27: 612–618

    Article  Google Scholar 

  19. Li S, Wang X. Finite-time consensus and collision avoidance control algorithms for multiple AUVs. Automatica, 2013, 49: 3359–3367

    Article  MathSciNet  MATH  Google Scholar 

  20. Braginsky B, Guterman H. Obstacle avoidance approaches for autonomous underwater vehicle: Simulation and experimental results. IEEE J Ocean Eng, 2016, 41: 882–892

    Article  Google Scholar 

  21. Saravanakumar S, Asokan T. Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space. Intel Serv Robotics, 2013, 6: 211–224

    Article  Google Scholar 

  22. Sullivan J, Waydo S, Campbell M. Using stream functions for complex behavior and path generation. In: Proceedings of AIAA guidance, navigation and control conference. Texas, 2003. 1–9

    Google Scholar 

  23. Yao P, Wang H, Su Z. UAV feasible path planning based on disturbed fluid and trajectory propagation. Chin J Aeronaut, 2015, 28: 1163–1177

    Article  Google Scholar 

  24. Zhu D, Huang H, Yang S X. Dynamic task assignment and path planning of multi-AUV system based on an improved self-organizing map and velocity synthesis method in three-dimensional underwater workspace. IEEE Trans Cybern, 2013, 43: 504–514

    Article  Google Scholar 

  25. Cui R, Li Y, Yan W. Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT. IEEE Trans Syst Man Cybern Syst, 2016, 46: 993–1004

    Article  Google Scholar 

  26. Cao X, Zhu D, Yang S X. Multi-AUV target search based on bioinspired neurodynamics model in 3-D underwater environments. IEEE Trans Neural Netw Learn Syst, 2016, 27: 2364–2374

    Article  MathSciNet  Google Scholar 

  27. Zeng Z, Lammas A, Sammut K, et al. Path planning for rendezvous of multiple AUVs operating in a variable ocean. In: The 4th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems. Hong Kong, 2014. 451–456

    Google Scholar 

  28. Shanmugavel M, Tsourdos A, White B, et al. Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs. Control Eng Practice, 2010, 18: 1084–1092

    Article  Google Scholar 

  29. Duan H, Zhang X, Wu J, et al. Max-min adaptive ant colony optimization approach to multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments. J Bionic Eng, 2009, 6: 161–173

    Article  Google Scholar 

  30. Makhdoom I H, Qin S Y. Simultaneous arrival of multiple UAVs under imperfect communication. Aircr Eng Aerosp Tech, 2012, 84: 37–50

    Article  Google Scholar 

  31. Lin Z, Liu H. Consensus based on learning game theory with a UAV rendezvous application. Chin J Aeronaut, 2015, 28: 191–199

    Article  Google Scholar 

  32. Li S, Wang X. Finite-time consensus algorithms for multiple AUVs. In: 32nd Chinese Control Conference (CCC). Xi’an, 2013. 5747–5752

    Google Scholar 

  33. Olfati-Saber R, Murray R M. Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans Automat Contr, 2004, 49: 1520–1533

    Article  MathSciNet  MATH  Google Scholar 

  34. Wu J, Wang H, Li N, et al. Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by adaptive grasshopper optimization algorithm. Aerosp Sci Tech, 2017, 70: 497–510

    Article  Google Scholar 

  35. Yao P, Wang H, Su Z. Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment. Aerosp Sci Tech, 2015, 47: 269–279

    Article  Google Scholar 

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Correspondence to Peng Yao.

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Yao, P., Qi, S. Obstacle-avoiding path planning for multiple autonomous underwater vehicles with simultaneous arrival. Sci. China Technol. Sci. 62, 121–132 (2019). https://doi.org/10.1007/s11431-017-9198-6

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  • DOI: https://doi.org/10.1007/s11431-017-9198-6

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